The importance of a routine health check-up for elderly patients, as well as the documentation of that check- up, cannot be overstated. Maintaining adequate documentation for a patient by himself or by his attendance, on the other hand, is extremely difficult. This paper primarily focuses on developing a framework that includes a smartphone application and introduces a real-time remote monitoring system for basic medical treatment of an elderly patient. It can keep track of a patient’s emergency contacts. Using the SMS manager API, emergency contacts can receive messages with the patient’s daily health alerts. Furthermore, the system will keep track of the patient’s symptoms, and if anything goes wrong for a continuous period of time, the system will automatically send out reminders to schedule medical appointments. For medical appointments, a patient may be able to search for nearest doctors and schedule an appointment for him via the system. Here, Google map technology is used to search nearby doctors. A reminder is introduced in this system where all of the activities for a patient are notified on a regular basis.
The practice of finding instances of semantic objects of a certain class, including people, cars, and traffic signs, in digital photos and videos is known as object identification or detection. Due to the development of high-resolution cameras and their widespread usage in everyday life, the detection is one of the most difficult and rapidly expanding study fields in computer science, particularly in computer vision. For automatic object recognition, several researchers have experimented with a variety of techniques, including image processing and computer vision. In this research, we employed a deep learning based framework YOLOv3 using Python, Tensorflow, and OpenCV to identify objects in real time. We do a number of tests using the COCO dataset to verify the effectiveness of the suggested strategy. The results of the experiments show that our suggested solution is resource and cost effective since it uses the fewest frames per second.
Liquefied petroleum gas (LPG) is used in a wide range of applications such as home and industrial appliances, vehicles, and refrigerators. However, leakage of gas can have a dangerous and toxic effect on humans and other living organisms. In this paper, an IoT based system is employed for this purpose to monitor gas leakage, detect flames, and alert users. The MQ-5 gas sensor was used to understand the concentration level of a closed volume of gas, while the infrared flame sensor was used to detect the spread of fire in this study. The proposed system has the capacity to detect fire and gas leaks as well as take additional action to lower gas concentration by air ventilation with exhausted fan and put out fires with fire extinguisher. The suggested approach will contribute to increasing safety, lowering the mortality toll, and minimizing harm to the environment. Overall system is implemented with IOT cloud-based remote controls to prevent gas leakage by using android application in response to individual feedback or feed-forward commands. The controller used here is Arduino Uno Rev3 SMD. This study provides design approaches to both software and hardware.
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